Wireless sensor networks (WSNs) demand low power and energy efficient hardware and software. Dynamic Power Management (DPM) technique reduces the maximum possible active states of a wireless sensor node by controlling the switching of the low power manageable components in power down or off states. During DPM, it is also required that the deadline of task execution and performance are not compromised. It is seen that operational level change can improve the energy efficiency of a system drastically (up to 90%). Hence, DPM policies have drawn considerable attention. This review paper classifies different dynamic power management techniques and focuses on stochastic modeling scheme which dynamically manage wireless sensor node operations in order to minimize its power consumption. This survey paper is expected to trigger ideas for future research projects in power aware wireless sensor network arenas.
Purpose
The use of Internet of Things (IoT) and networks has built a potential impact on the product cost and time in a company’s manufacturing process. These IoT solutions provide end-to-end visibility and faster introduction of merchandise and supplier in the market. The main aim of this research paper is to supply products with improved quality and cheaper price, whereas the rising response and quality of the client service.
Design/methodology/approach
This paper designs and develops two cases for selecting the most efficient vendor while keeping in mind the profit and cost constraints in optimization.
Findings
Outsourcing is a vital parameter to cut back the price and maximize the profit of the manufacturer. Therefore, the integration of supply chain with IoT can provide a solution to the cost optimization and supplier/vendor selection problems in supply chain management.
Research limitations/implications
The results show that the models are quite realistic and can help the IoT-based manufacturing units to make strategic decisions regarding product manufacturing and distribution.
Practical implications
The authors can further extend the model to derive the retailer’s profit function and develop the end product cost to the consumers and hence make it a n-level multi-vendor selection model for IoT-based systems.
Originality/value
The right choice of vendor for IoT-enabled business is a crucial concern. In this paper, the authors designed and developed multi-vendor models with in-house production and outsourcing decisions to meet the demand along with the vendor selection. The variable demands and designed variable unit cost function and batch order are set to make vendor selection more realistic.
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